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Wind power forecast using neural networks: Tuning with optimization techniques and error analysis
(2020-03-01)
The increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power ...
Cross-validation based forecasting method: a machine learning approach
(2019-02)
Our paper aims to evaluate two novel methods on selecting the best forecasting model or its combination based on a Machine Learning approach. The methods are based on the selection of the ”best” model, or combination of ...
Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting
(Institute of Electrical and Electronics Engineers (IEEE), 2012-11-01)
A multi-agent system for spatial electric load forecasting, especially suited to simulating the different social dynamics involved in distribution systems, is presented. This approach improves the spatial forecasting ...
Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting
(Institute of Electrical and Electronics Engineers (IEEE), 2012-11-01)
A multi-agent system for spatial electric load forecasting, especially suited to simulating the different social dynamics involved in distribution systems, is presented. This approach improves the spatial forecasting ...
Fuzzy Time Series Methods Applied to Short -Term Photovoltaic Power Forecasting Forecasting
(, 2021)
Abstract— Solar photovoltaic energy has shown a significant growth in the last decade. In the face of this growth, there are challenges to consider for the high penetration rates of solar photovoltaic, since this type of ...
A fast electric load forecasting using adaptive neural networks
(2003-12-01)
This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, ...
A fast electric load forecasting using adaptive neural networks
(2003-12-01)
This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, ...
Does curvature enhance forecasting?
(Banco Central do Brasil, 2008)
In this paper, we analyze the importance of curvature term structure movements on forecasts of interest rates. An extension of the exponential three-factor Diebold and Li (2006) model is proposed, where a fourth factor ...
A Factor Augmented Vector Autoregressive model and a Stacked De-noising Auto-encoders forecast combination to predict the price of oil
(2019-01-24)
A dissertação a seguir tem como objetivo mostrar os benefícios de uma combinação de previsão entre uma metodo econométrico e um de Deep Learning. De um lado, um Factor Augmented Vector Autoregressive (FAVAR) com identificação ...
Comparison between the Operational and Statistical Daily Maximum and Minimum Temperature Forecasts on the Central Coast of Peru
(AMS, 2023-04-06)
Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational ...